International Journal of Industrial Engineering: Theory, Applications and Practice

The Impact of Corporate Social Responsibility on Sustainable Development Performance - Mediating Effect of Dual Green Innovation

2024-12-08T18:08:07-08:00

Green sustainable development remains the cornerstone of China's economic progress and the guiding principle for the transformation and advancement of its manufacturing sector. Aligned with China's objectives of achieving a "carbon peak" by 2030 and "carbon neutrality" by 2060, expediting the green development of manufacturing enterprises and enhancing their sustainable development performance are crucial to ensuring their long-term health and prosperity amid transformation and upgrading. This holds the key to realizing sustained and healthy growth for manufacturing enterprises as they navigate the process of transformation and advancement. According to stakeholder theory and natural resource-based theory, this study evaluates Chinese listed manufacturing enterprises, utilizing panel data spanning 2012-2021 to empirically analyze the relationship between CSR, dual green innovation, sustainable development performance, and redundant resources in these enterprises. A model is constructed to explain the effect of CSR on sustainable development performance. The findings demonstrate that CSR significantly cultivates both continuous and disruptive green innovation, elevating corporate sustainability performance. Continuous and disruptive green innovation represents positive mediating factors in this relationship. The moderating effect analysis indicates that non-sedimentary redundant resources exert a positive moderating effect between CSR and dual green innovation, while the moderating effect of sedimentary redundant resources between the two remains insignificant. Further exploration indicates that in the short term, continuous green innovation exhibits a more significant and positive effect on sustainability performance. Conversely, in the long run, disruptive green innovation demonstrates a greater positive effect on sustainability performance. Considering these conclusions and the specific characteristics and requirements of manufacturing enterprises, this paper proposes relevant recommendations to assist enterprises in effectively enhancing their sustainable development performance.

Optimization of Inventory Replenishment under Asymmetric Stock-Out and Inventory Holding Costs

2024-08-29T18:41:11-07:00

Perishable products are an essential part of commerce. Shelf-life characteristics are usually not modeled in traditional inventory models. This study proposes an inventory replenishment model for perishable products with an asymmetric cost structure for holding and stock-out costs. The modeling phase involves the shelf-life characteristics of products. Shelf life is essential due to sustainability concerns, costs, and service levels due to perished products. In contrast to classical safety stock models, where stock-out costs increase linearly, the proposed model utilizes incrementally increased fixed costs for holding costs in a conflicting cost structure. It incorporates the shelf-life of the products, calculates the probability of perishing, and formulates accurate waste and total costs using an asymmetrical cost structure. The model is applied to a real dataset to assess the performance and compare it with the traditional approach. The performance of the proposed model is better, with a total cost reduction of 45.33%. Additionally, the model demonstrated a 17.21% increase in service level. The sensitivity analysis further underlined the robustness of the proposed model across various demand scenarios and shelf-life conditions. The main research gap addressed by this study is the lack of consideration for shelf-life characteristics and asymmetric cost structures in traditional inventory models. By integrating these factors, this research provides a more accurate and cost-effective approach to inventory management for perishable products, enhancing sustainability and service levels. This study's findings can help businesses optimize inventory strategies, reduce waste, and improve operational efficiency.

Jointly Optimizing Parallel Batch Processing Scheduling in A Semiconductor Manufacturing Environment

2024-12-23T18:20:58-08:00

The rapid growth of the semiconductor industry has led to high water and energy consumption and substantial greenhouse gas emissions. Achieving sustainability in the semiconductor industry has become an exceedingly important issue. This paper investigates a complex batch processing scheduling problem in the final testing phase of semiconductor manufacturing, where chambers and chips are modeled as batch processing machines and jobs. Machines can process multiple jobs simultaneously, with each job defined by its processing time, release time, and size. A mixed-integer linear programming model is presented, along with a constructive-based metaheuristic, the ACS-PBPMs algorithm, to optimize batch formation and scheduling decisions jointly. The algorithm uses an effective candidate list strategy to address constraints and incorporates a local search phase based on solution characteristics. Experimental results on diverse problem instances show that the ACS-PBPMs algorithm outperforms CPLEX and competitive algorithms in computational efficiency and solution quality.

A Reinforcement Learning and The Northern Goshawk Optimization Algorithm for Flexible Job Shop Scheduling Problem

2024-12-08T18:23:55-08:00

This paper introduces northern goshawk optimization, a novel global search strategy for the flexible job shop scheduling problem. It uses two-stage encoding and random-key-based encoding to transform individual position vectors into flexible job shop scheduling problem solutions. To improve local search, reinforcement learning is integrated, converting the flexible job shop scheduling problem into a Markov decision process with 10 states and 6 rules. A reward function based on optimal completion time guides the search. The proposed hybrid northern goshawk optimization-Q-learning-state-action-reward-state-action framework combines global and local search strengths. Experiments on standard datasets show the algorithm's superior performance, validating its effectiveness and practicality in solving the flexible job shop scheduling problem and real-world production scheduling problems.

Optimal Control Strategies for Adaptive Pricing in Ride-Sharing Services

2024-09-22T19:03:50-07:00

Rideshare platforms are an example of economies of sharing where ride requests initiated by riders are fulfilled by car owners through the platform that connects both of them. When demand for a ride is initiated by the customer, the platform checks service providers' (car owners) availability and assigns a fare (ride price) that both the ride requester and provider should agree on to complete the transaction, and the ride service is fulfilled. In this research, optimal pricing strategies for ride-share platforms are considered. The optimal control approach is used to first develop differential equations to model the dynamics of the number of ride requests and for the price rate. Second, we model the total profit as a function of a linear revenue and a nonlinear cost. The optimal rate of change in the ride price is then obtained. Finally, a numerical example and extensive sensitivity analyses not only provide insights into the effect of the system parameter on the model but also lead to managerial implications to help companies determine the best price for each ride.

A New Loss Function Based on BURR XII for Using Risk Estimation

2024-12-23T18:28:04-08:00

Achieving a competitive advantage in today's fast-paced and globally competitive business environment is one of the main goals for organizations across all industries. To attain this, businesses must adopt effective strategies and tools to enhance their operational efficiency, product quality, and overall customer satisfaction. One of the most significant tools that can help businesses achieve this goal is the implementation of quality control methods. Quality control not only ensures that products and services meet predefined standards but also helps reduce costs, increase customer loyalty, and maintain a sustainable competitive edge. In recent years, the importance of loss functions in quality assurance has grown considerably. Loss functions are used to quantify the deviation of a product's performance or quality from its desired target, translating this deviation into a monetary loss. This concept enables businesses to assess the broader impact of poor quality, not only on the organization but also on society as a whole. The monetary value of the loss represents the cost associated with a product's failure to meet expectations, including customer dissatisfaction, warranty claims, and potential reputational damage. Advancements in statistical methodologies, particularly those involving inverted probability density functions (PDFs), have opened new avenues for the application of loss functions. Inverted PDFs allow for a more detailed understanding of quality-related losses. This paper introduces the Inverted Burr XII Loss Function (IBXIILF) as a novel member of the inverted probability loss function family. The IBXIILF provides a robust framework for evaluating and minimizing quality-related losses in various industrial settings. The performance and applicability of the IBXIILF are demonstrated through a comparative study and an industrial example, highlighting its practical relevance and effectiveness in monitoring losses.

A Novel Multimodal Transport Model for School Commuting

2024-12-23T18:04:35-08:00

During peak hours, the roads surrounding schools often become a source of concern due to severe traffic congestion. This not only leads to a substantial increase in travel time but also poses heightened safety risks. This study proposes a multimodal transport model that integrates private cars, shared parking spaces, and school buses (CPB) to address these challenges. The model aims to improve traffic efficiency and reduce safety hazards. The approach involves two key phases: identifying optimal locations for private car parking and optimizing school bus routes. Results show an 18.53% reduction in private car costs and an 8.13% decrease in traffic delays within the road network. The advantages of this model become particularly significant when student commuting demand exceeds 70% of peak transportation demand. This study provides a robust scientific foundation for developing traffic management strategies around schools.

Global Path Planning Method for AGV of Warehousing Logistics Based on Improved Ant Colony Algorithm

2024-09-03T19:03:04-07:00

AGV for warehousing and logistics is an automatic guided vehicle that is used for cargo handling, storage, sorting and other operations in warehousing and logistics scenarios. Due to the complex warehousing logistics scenarios, AGV needs to deal with the complex environment and variable task requirements in warehousing logistics during operation, resulting in low efficiency of path planning. Therefore, a global path planning method for AGV of warehousing logistics based on an improved ant colony algorithm is studied. After analyzing the overall transportation path of warehousing logistics, according to the optimization algorithm of ants' foraging behavior in nature, the pheromone transmission mechanism and behavior rules are simulated, and relevant factors such as path length transportation efficiency. Obstacle avoidance and load balance are considered to adjust the parameters such as pheromone volatilization factor and heuristic information weighting so that the improved ant colony algorithm can better adapt to changes in the warehousing logistics environment and improve the accuracy and reliability of AGV path planning. Through experimental verification, the effectiveness and superiority of the method are proved, the AGV transportation efficiency is improved, and the algorithm has excellent stability and adaptability.

A study of Optimal Assignment Model Considering Quality and Worker Level in Limited-Cycle with Multiple Periods For Smart Manufacturing

2024-11-12T18:31:11-08:00

In recent years, quality irregularities reported by various media have shaken public confidence in companies, impacting both sales and stock prices, even among global leaders in manufacturing. Additionally, the increase in foreign workers and variations in service length have introduced differences in worker skill levels. This study aims to identify optimal worker assignment strategies that balance quality and worker skill levels for efficient production. We propose an optimal assignment model for a smart production line in a limited-cycle, multi-period setting that considers both quality and due dates. Numerical experiments further analyze optimal arrangement strategies and characteristics across different worker skill levels.

Strategic Choices of Online Retailers in Live-Streaming E-Commerce

2024-12-23T17:51:20-08:00

Some online retailers, based on traditional channels, have launched live-streaming channels. To study the optimal live-streaming choices of online retailers, this paper models the supply chain composed of a manufacturer and an online retailer by establishing three modes: no live-streaming mode, influencer live-streaming mode, and retailer self-live-streaming mode. The online retailer's optimal choice of live-streaming mode is derived by analyzing the equilibrium solutions. The results suggest that when the sales ability of the employee-streamer and the live-streaming consumer purchase rate are high, it's always advantageous to opt for retailer self-live-streaming mode. Affected by the influencer's commission rate and fixed participation fee, the influencer live-streaming mode is harmful to the online retailer. However, if the influencer chooses a profit-sharing mechanism, the online retailer and influencer can achieve a Pareto profit improvement, and online retailers will open the influencer live-streaming mode.

A Two-stage Algorithm for Production Distribution Optimization of Fresh Products

2024-10-07T18:00:06-07:00

The rise of e-commerce and the just-in-time system has imposed more stringent demands on fresh product supply chains. This paper addresses the challenges of production and distribution decision-making under uncertainty, considering the vehicle routing problem with time windows (VRPTW). Fresh products are distributed immediately after production, with any remaining perishable products deteriorating before they can be transported. To address these issues, a mathematical model is proposed for optimizing the production and distribution of fresh products. The objective optimization model for production scheduling and VRPTW is classified as an NP-hard problem. To tackle and optimize this complex problem, a two-stage algorithm combining ant colony optimization (ACO) and a fuzzy adaptive genetic algorithm (FAGA) is proposed. The approach begins by determining the critical combination parameters of the algorithm. Subsequently, analysis of the model's results reveals that production and distribution costs decrease significantly when integrated decision-making is employed. Additionally, the vehicle setup cost introduces a turning point in the overall target cost. Finally, a numerical experiment on VRPTW is conducted, with the results demonstrating the effectiveness of the proposed two-stage algorithm.

Channel Coordination of Dual-Channel Supply Chain with C2M Manufacturer Introducing The Private Label

2024-10-22T19:46:05-07:00

The thriving development of E-commerce platforms has injected new vitality into the C2M (Customer-to-Manufacturer) business model. However, it has also introduced a new challenger-the private label retailer. Given the escalating influence of private label retailers, the strategic introduction and coordination of the supply chain by C2M manufacturers have emerged as pivotal challenges. We analyze a C2M manufacturer's pricing strategies in three dual-channel supply chain models: centralization, decentralization, and partial centralization. In these models, the C2M manufacturer acts as a Stackelberg leader, while the private label retailer and platform act as followers. Analysis of both symmetric and asymmetric channel scenarios demonstrates that C2M manufacturers favor centralizing the supply chain. Consequently, we explore coordination strategies for Dual-channel supply chains and determine that the C2M manufacturer's contract, which incorporates wholesale and direct channel pricing, effectively coordinates the dual-channel supply, benefiting the retailer and platform but not the C2M manufacturer. We illustrate how such a contract, coupled with a complementary agreement like a two-part tariff or profit-sharing, can effectively coordinate the dual-channel supply chain, creating a win-win-win situation for the C2M manufacturer, private label retailer, and platform.

A Customer Demand Mining Algorithm Based on Online Comments and Machine Learning

2024-08-01T18:37:47-07:00

In the current market environment, the phenomenon of product homogenization is severe. If enterprises cannot deeply understand customer needs and provide differentiated products or services, it is difficult to stand out in the competition. In order to effectively improve overall customer satisfaction and enhance the market competitiveness of enterprises, a customer demand mining algorithm based on online comments and machine learning is proposed. Collect customer demand information data through online comments and process the collected data with redundant information to improve the efficiency and accuracy of demand mining. On this basis, customer demand attribute features have been further extracted, and a customer demand clustering mining model has been constructed using a self-organizing mapping neural network. By training the model, the final clustering mining results can be obtained, thus achieving precise mining of customer needs. This study clearly addresses a key issue in the current field of consumer demand mining: how to efficiently and accurately identify and utilize consumer demand information in online comments. By constructing a clustering mining model based on the Self-Organizing Maps (SOM) neural network, this study fills the literature gap in this field and provides more accurate and practical consumer demand analysis methods for enterprises. The experimental results show that, compared with the three comparison methods, the proposed method has a 98% feasibility of customer demand mining and 92% customer satisfaction. It shows that the proposed method has high feasibility and customer satisfaction for customer demand mining and has a better overall customer demand mining effect. This provides strong support for improving overall customer satisfaction and corporate competitiveness.

DCNN-BIGRU: A Proficient Hybrid Classifier for Reliable Intrusion Detection and Prevention: Hybrid Approach

2024-09-11T18:45:42-07:00

Advances in networking devices have revolutionized many industries by enabling intercommunication and automation in multiple areas, such as healthcare, transportation, and manufacturing. However, the threat of cyber-attacks has also escalated with the increased connectivity and dependency on these devices. Cyber security has become critical in protecting networks from malicious activities, ensuring the privacy and integrity of the data transmitted. Multiple deep-learning methods face multiple challenges in identifying intrusion threats; however, deep learning can self-enhance and scale up for reliability. We propose an efficient hybrid deep-learning intrusion-detection classifier, DCNN-BiGRU. The classifier has a simple architecture and works well in environments that do not require saving long-term dependencies and where computational resources are limited. It achieved a multiclass-classification accuracy of 99.70% on the training and test datasets.